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Nisa Kelly
Clinical Research Coordinator
Background in biochemistry and medical health sciences; coordinated the Shape Up! Studies. Co-author on publications examining optical imaging for pediatric body composition.
Publications (35)
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3D convolutional deep learning for nonlinear estimation of body composition from whole body morphology
Tian IY, Liu J, Wong MC, Kelly NN, Liu YE, Garber AK, Heymsfield SB, Curless B, Shepherd J
npj Digital Medicine, 2025· doi:10.1038/s41746-025-01469-6 -
3D Convolutional Deep Learning for Nonlinear Estimation of Body Composition from Whole-Body Morphology
Tian IY, Liu J, Wong MC, Kelly NN, Liu Y, Garber AK, Heymsfield SB, Curless B, Shepherd J
Research Square, 2024· doi:10.21203/rs.3.rs-3935042/v1 -
Evaluation of body shape as a human body composition assessment in isolated conditions and remote environments
Wong MC, Bennett JP, Leong LT, Liu YE, Kelly NN, Cherry JA, Kloza K, Li B, Iuliano S, Sibonga JD, Sawyer A, Ayton J, Shepherd J
npj Microgravity, 2024· doi:10.1038/s41526-024-00412-5 -
Trunk-to-leg volume and appendicular lean mass from a commercial 3-dimensional optical body scanner for disease risk identification
Bennett JP, Wong MC, Liu YE, Quon BK, Kelly NN, Garber AK, Heymsfield SB, Shepherd J
Clinical Nutrition, 2024· doi:10.1016/j.clnu.2024.09.028 -
Generative deep learning furthers the understanding of local distributions of fat and muscle on body shape and health using 3D surface scans
Leong LT, Wong MC, Liu YE, Glaser Y, Quon BK, Kelly NN, Cataldi D, Sadowski P, Heymsfield SB, Shepherd J
Communications Medicine, 2024· doi:10.1038/s43856-024-00434-w -
Variations in bioelectrical impedance devices impact raw measures comparisons and subsequent prediction of body composition using recommended estimation equations
Bennett JP, Cataldi D, Liu YE, Kelly NN, Quon BK, González MC, Heymsfield SB, Shepherd J
Clinical Nutrition ESPEN, 2024· doi:10.1016/j.clnesp.2024.07.009 -
Accuracy and Precision of Multiple Laboratory and Field Methods to The Criterion In Vivo Five-Compartment Body Composition Model and Their Association with Muscle Strength in Collegiate Athletes of Varying States of Hydration: The Da Kine Protocol Study
Cataldi D, Bennett JP, Wong MC, Quon BK, Liu YE, Kelly NN, Kelly TL, Schoeller DA, Heymsfield SB, Shepherd J
medRxiv, 2023· doi:10.1101/2023.05.30.23290630 -
Deep Learning Furthers the Understanding of Local Distributions of Fat and Muscle on Body Shape and Health Using 3D Surface Scans
Leong LT, Wong MC, Liu YE, Glaser Y, Quon BK, Kelly NN, Cataldi D, Sadowski P, Heymsfield SB, Shepherd J
SSRN Electronic Journal, 2023· doi:10.2139/ssrn.4436398 -
Reply to Y Lu et al.
Bennett JP, Liu YE, Kelly NN, Quon BK, Wong MC, McCarthy C, Heymsfield SB, Shepherd J
American Journal of Clinical Nutrition, 2023· doi:10.1016/j.ajcnut.2023.01.004 -
Development and validation of a rapid multicompartment body composition model using 3-dimensional optical imaging and bioelectrical impedance analysis
Bennett JP, Cataldi D, Liu YE, Kelly NN, Quon BK, Schoeller DA, Kelly TL, Heymsfield SB, Shepherd J
Clinical Nutrition, 2023· doi:10.1016/j.clnu.2023.12.009 -
Automated body composition estimation from device-agnostic 3D optical scans in pediatric populations
Tian IY, Wong MC, Nguyen WM, Kennedy S, McCarthy C, Kelly NN, Liu YE, Garber AK, Heymsfield SB, Curless B, Shepherd J
Clinical Nutrition, 2023· doi:10.1016/j.clnu.2023.07.012 -
Cross-sectional assessment of body composition and detection of malnutrition risk in participants with low body mass index and eating disorders using 3D optical surface scans
Garber AK, Bennett JP, Wong MC, Tian IY, Maskarinec G, Kennedy S, McCarthy C, Kelly NN, Liu YE, Machen VI, Heymsfield SB, Shepherd J
American Journal of Clinical Nutrition, 2023· doi:10.1016/j.ajcnut.2023.08.004 -
Accuracy and precision of multiple body composition methods and associations with muscle strength in athletes of varying hydration: The Da Kine Study
Cataldi D, Bennett JP, Wong MC, Quon BK, Liu YE, Kelly NN, Kelly TL, Schoeller DA, Heymsfield SB, Shepherd J
Clinical Nutrition, 2023· doi:10.1016/j.clnu.2023.11.040 -
Accuracy and Precision of 3-dimensional Optical Imaging for Body Composition by Age, BMI, and Ethnicity
Wong MC, Bennett JP, Quon BK, Leong LT, Tian IY, Liu YE, Kelly NN, McCarthy C, Chow DC, Pujades S, Garber AK, Maskarinec G, Heymsfield SB, Shepherd J
American Journal of Clinical Nutrition, 2023· doi:10.1016/j.ajcnut.2023.07.010 -
Monitoring body composition change for intervention studies with advancing 3D optical imaging technology in comparison to dual-energy X-ray absorptiometry
Wong MC, Bennett JP, Leong LT, Tian IY, Liu YE, Kelly NN, McCarthy C, Wong JM, Ebbeling CB, Ludwig DS, Irving BA, Scott MC, Stampley J, Davis B, Johannsen NM, Matthews R, Vincellette C, Garber AK, ...
American Journal of Clinical Nutrition, 2023· doi:10.1016/j.ajcnut.2023.02.006 -
Accuracy and Precision of 3D Optical Imaging for Body Composition and their Associations to Metabolic Markers by Age, BMI, and Ethnicity
Wong MC, Bennett JP, Quon BK, Leong LT, Tian IY, Liu YE, Kelly NN, McCarthy C, Chow DC, Pujades S, Garber AK, Maskarinec G, Heymsfield SB, Shepherd J
medRxiv, 2022· doi:10.1101/2022.11.02.22281819 -
A device‐agnostic shape model for automated body composition estimates from 3D optical scans
Tian IY, Wong MC, Kennedy S, Kelly NN, Liu YE, Garber AK, Heymsfield SB, Curless B, Shepherd J
Medical Physics, 2022· doi:10.1002/mp.15843 -
Three‐dimensional optical body shape and features improve prediction of metabolic disease risk in a diverse sample of adults
Bennett JP, Liu YE, Quon BK, Kelly NN, Leong LT, Wong MC, Kennedy S, Chow DC, Garber AK, Weiss EJ, Heymsfield SB, Shepherd J
Obesity, 2022· doi:10.1002/oby.23470 -
Next-generation smart watches to estimate whole-body composition using bioimpedance analysis: accuracy and precision in a diverse, multiethnic sample
Bennett JP, Liu YE, Kelly NN, Quon BK, Wong MC, McCarthy C, Heymsfield SB, Shepherd J
American Journal of Clinical Nutrition, 2022· doi:10.1093/ajcn/nqac200 -
Accessible Five Compartment Body Composition via 3-Dimensional Imaging and Bioelectrical Impedance
Bennett JP, Cataldi D, Quon BK, Liu YE, Kelly NN, KELLY T, Heymsfield SB, Garber AK, Weiss EJ, Shepherd J
, 2021· doi:10.15221/21.39 -
Evaluating the Accuracy of an Hallucinatory Algorithm to Predict Body Shape Changes from Dieting and Physical Activity
Shepherd J, Wong MC, Tian IY, Liu YE, Kennedy S, LOWE D, Kelly NN, Wong JM, Ebbeling CB, Ludwig DS, Weiss EJ, Curless B, Heymsfield SB
, 2021· doi:10.15221/21.43 -
Creating Accurate Representations of DXA Scans from 3D Optical Body Surface Scans for Arbitrary Regional Body Composition Analysis
Leong LT, Wong MC, Liu YE, Kelly NN, PIAZZA M, GARRY S, Heymsfield SB, Shepherd J
, 2021· doi:10.15221/21.35 -
3D Optical Body Composition Accuracy across Subgroups of BMI and Race/Ethnicity
Wong MC, LIU YE, Kennedy S, Kelly NN, Maskarinec G, Garber AK, Wong JM, Ebbeling CB, Ludwig DS, Weiss EJ, Pujades S, Heymsfield SB, Shepherd J
, 2021· doi:10.15221/21.36 -
Predicting Muscular Strength with 3D Optical in a Diverse Adult Population
Cataldi D, Bennett J, Quon BK, Liu YE, Kelly NN, Heymsfield SB, Shepherd J
, 2021· doi:10.15221/21.38 -
Digital anthropometric evaluation of young children: comparison to results acquired with conventional anthropometry
Kennedy S, Smith B, Sobhiyeh S, Dechenaud M, Wong MC, Kelly NN, Shepherd J, Heymsfield SB
European Journal of Clinical Nutrition, 2021· doi:10.1038/s41430-021-00938-x